Overview

Dataset statistics

Number of variables12
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.5 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

monetary is highly overall correlated with qtd_compras and 3 other fieldsHigh correlation
qtd_compras is highly overall correlated with monetary and 3 other fieldsHigh correlation
qtd_items is highly overall correlated with monetary and 3 other fieldsHigh correlation
qtd_prods is highly overall correlated with monetary and 3 other fieldsHigh correlation
recency is highly overall correlated with qtd_comprasHigh correlation
avg_ticket is highly overall correlated with unique_avg_basketHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
unique_avg_basket is highly overall correlated with qtd_prods and 1 other fieldsHigh correlation
avg_basket is highly overall correlated with monetary and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.44422362)Skewed
frequency is highly skewed (γ1 = 24.88049136)Skewed
qtd_returns is highly skewed (γ1 = 51.79774426)Skewed
avg_basket is highly skewed (γ1 = 44.67271661)Skewed
customer_id has unique valuesUnique
recency has 34 (1.1%) zerosZeros
qtd_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-06-11 19:37:16.154835
Analysis finished2023-06-11 19:37:37.897034
Duration21.74 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:37.993205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityStrictly increasing
2023-06-11T16:37:38.144298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12347 1
 
< 0.1%
16261 1
 
< 0.1%
16243 1
 
< 0.1%
16244 1
 
< 0.1%
16245 1
 
< 0.1%
16249 1
 
< 0.1%
16250 1
 
< 0.1%
16253 1
 
< 0.1%
16255 1
 
< 0.1%
16256 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

monetary
Real number (ℝ)

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.3217
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:38.292288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.623
Coefficient of variation (CV)3.8484486
Kurtosis353.94472
Mean2749.3217
Median Absolute Deviation (MAD)672.16
Skewness16.777556
Sum8162736.2
Variance1.1194959 × 108
MonotonicityNot monotonic
2023-06-11T16:37:38.430491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
734.94 2
 
0.1%
533.33 2
 
0.1%
379.65 2
 
0.1%
331 2
 
0.1%
2092.32 2
 
0.1%
889.93 2
 
0.1%
598.2 2
 
0.1%
1078.96 2
 
0.1%
745.06 2
 
0.1%
1314.45 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

qtd_compras
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7231391
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:38.583397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8565313
Coefficient of variation (CV)1.5474954
Kurtosis190.83445
Mean5.7231391
Median Absolute Deviation (MAD)2
Skewness10.766805
Sum16992
Variance78.438147
MonotonicityNot monotonic
2023-06-11T16:37:38.927929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtd_items
Real number (ℝ)

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.8525
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:39.076232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.4
Q1296
median641
Q31401
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1105

Descriptive statistics

Standard deviation5887.578
Coefficient of variation (CV)3.6594891
Kurtosis465.99808
Mean1608.8525
Median Absolute Deviation (MAD)422
Skewness17.858591
Sum4776683
Variance34663575
MonotonicityNot monotonic
2023-06-11T16:37:39.232357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
272 8
 
0.3%
246 8
 
0.3%
260 8
 
0.3%
84 8
 
0.3%
288 8
 
0.3%
219 7
 
0.2%
114 7
 
0.2%
Other values (1661) 2886
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%

qtd_prods
Real number (ℝ)

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.72415
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:39.398021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.89641
Coefficient of variation (CV)2.1992119
Kurtosis354.86113
Mean122.72415
Median Absolute Deviation (MAD)44
Skewness15.707635
Sum364368
Variance72844.071
MonotonicityNot monotonic
2023-06-11T16:37:39.547658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
29 35
 
1.2%
35 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 30
 
1.0%
Other values (458) 2629
88.5%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

recency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.287639
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:39.714343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756779
Coefficient of variation (CV)1.2095137
Kurtosis2.7779627
Mean64.287639
Median Absolute Deviation (MAD)26
Skewness1.7983795
Sum190870
Variance6046.1167
MonotonicityNot monotonic
2023-06-11T16:37:39.863009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
22 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.897762
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:40.015846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.956587
Q324.988286
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation1036.9344
Coefficient of variation (CV)19.98033
Kurtosis2890.7071
Mean51.897762
Median Absolute Deviation (MAD)5.984842
Skewness53.444224
Sum154084.45
Variance1075233
MonotonicityNot monotonic
2023-06-11T16:37:40.154468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.47833333 2
 
0.1%
15 2
 
0.1%
4.162 2
 
0.1%
23.68131868 1
 
< 0.1%
38.54451852 1
 
< 0.1%
8.021568627 1
 
< 0.1%
16.73351648 1
 
< 0.1%
15.70733333 1
 
< 0.1%
26.08797101 1
 
< 0.1%
16.22666667 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.348511
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:40.308059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.923077
median48.285714
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.410256

Descriptive statistics

Standard deviation63.544929
Coefficient of variation (CV)0.94352388
Kurtosis4.8871091
Mean67.348511
Median Absolute Deviation (MAD)26.285714
Skewness2.0627709
Sum199957.73
Variance4037.958
MonotonicityNot monotonic
2023-06-11T16:37:40.454149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
11 17
 
0.6%
21 17
 
0.6%
46 17
 
0.6%
6 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1137973
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:40.610380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088941642
Q10.016339869
median0.025889968
Q30.049450549
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.03311068

Descriptive statistics

Standard deviation0.40815625
Coefficient of variation (CV)3.5866953
Kurtosis989.36508
Mean0.1137973
Median Absolute Deviation (MAD)0.012191338
Skewness24.880491
Sum337.8642
Variance0.16659153
MonotonicityNot monotonic
2023-06-11T16:37:40.759713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.08333333333 15
 
0.5%
0.09090909091 15
 
0.5%
0.02941176471 14
 
0.5%
0.03448275862 14
 
0.5%
0.07692307692 13
 
0.4%
0.03571428571 13
 
0.4%
Other values (1215) 2636
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtd_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.156955
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:40.919366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.4961
Coefficient of variation (CV)24.333498
Kurtosis2765.5286
Mean62.156955
Median Absolute Deviation (MAD)1
Skewness51.797744
Sum184544
Variance2287644.6
MonotonicityNot monotonic
2023-06-11T16:37:41.077505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

unique_avg_basket
Real number (ℝ)

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.484591
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:41.241613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.6666667
median13.6
Q322.142857
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.47619

Descriptive statistics

Standard deviation15.460307
Coefficient of variation (CV)0.8842247
Kurtosis29.317441
Mean17.484591
Median Absolute Deviation (MAD)6.6
Skewness3.4358615
Sum51911.752
Variance239.02111
MonotonicityNot monotonic
2023-06-11T16:37:41.395950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 42
 
1.4%
9 41
 
1.4%
8 39
 
1.3%
16 39
 
1.3%
17 38
 
1.3%
14 38
 
1.3%
7 36
 
1.2%
11 36
 
1.2%
5 36
 
1.2%
15 35
 
1.2%
Other values (896) 2589
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

avg_basket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.81376
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-11T16:37:41.557137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.33333
Q3281.69231
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.44231

Descriptive statistics

Standard deviation791.55519
Coefficient of variation (CV)3.1685812
Kurtosis2255.5382
Mean249.81376
Median Absolute Deviation (MAD)83.083333
Skewness44.672717
Sum741697.07
Variance626559.62
MonotonicityNot monotonic
2023-06-11T16:37:41.723728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
86 9
 
0.3%
82 9
 
0.3%
73 9
 
0.3%
136 8
 
0.3%
60 8
 
0.3%
75 8
 
0.3%
88 8
 
0.3%
197 7
 
0.2%
Other values (1969) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

Interactions

2023-06-11T16:37:35.518116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:16.413263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:18.070528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:19.909509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:21.891221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:23.752970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:25.467058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:27.272892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:28.813595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:30.424931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:32.197248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:33.900299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:35.674698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:16.549995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:18.199184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:20.073043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:22.043807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:23.889011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:25.601697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:27.394965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:28.943261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:30.555516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:32.336486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:34.023007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:35.830282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:16.678616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:18.322613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:20.239595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:22.304107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:24.025189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:25.733309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:27.517661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:29.072937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:30.684177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:32.467133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:34.144925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:35.971903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:16.798296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:18.446718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:20.403159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:22.440716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:24.154908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:25.876925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:27.635346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:29.192066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:30.805839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:32.593794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:34.263600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:36.153448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:16.942995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:18.603009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:20.581683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:22.588181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:24.306435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:26.024530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:27.772014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:29.336635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:30.943444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:32.738405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:34.401139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:36.336926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:17.084325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:18.763584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:20.761203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:22.736809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:24.455038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:26.172100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:27.906654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:29.476428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:31.086063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:32.882057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:34.541756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:36.501522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:17.231906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:18.919128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:20.908814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:22.879427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:24.596624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:26.310564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:28.038621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:29.610104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:31.221699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:33.022682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:34.686341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:36.646100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:17.363486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:19.058784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:21.041452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:23.018056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:24.734255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:26.440741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:28.158348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:29.740754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:31.349359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:33.151341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:34.816991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:36.805673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:17.513382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:19.204368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:21.199047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:23.166659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:24.877871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:26.590303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:28.290993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:29.875519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:31.489019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:33.290963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:34.954624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:36.958265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:17.654006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:19.366947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:21.378550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:23.315267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:25.024440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:26.862607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:28.423674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:30.012261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:31.622575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:33.432552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:35.090261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:37.136788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:17.795806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:19.555447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:21.560084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:23.464740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:25.177027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:27.006612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:28.556284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:30.158873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:31.766147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:33.574172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:35.236904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:37.296379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:17.920945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:19.727994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:21.718673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:23.601377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:25.313996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:27.132275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:28.676963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:30.285451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:31.892063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:33.707850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T16:37:35.360538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-11T16:37:41.866455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
customer_idmonetaryqtd_comprasqtd_itemsqtd_prodsrecencyavg_ticketavg_recency_daysfrequencyqtd_returnsunique_avg_basketavg_basket
customer_id1.000-0.0760.026-0.0700.0130.001-0.1310.019-0.002-0.063-0.016-0.123
monetary-0.0761.0000.7700.9250.744-0.4150.246-0.2470.0900.3720.1040.574
qtd_compras0.0260.7701.0000.7160.690-0.5020.059-0.2590.0790.294-0.1810.100
qtd_items-0.0700.9250.7161.0000.730-0.4080.167-0.2270.0800.3440.1470.729
qtd_prods0.0130.7440.6900.7301.000-0.435-0.377-0.1660.0360.2420.5160.383
recency0.001-0.415-0.502-0.408-0.4351.0000.0480.1080.018-0.1200.015-0.098
avg_ticket-0.1310.2460.0590.167-0.3770.0481.000-0.1220.0900.190-0.6180.188
avg_recency_days0.019-0.247-0.259-0.227-0.1660.108-0.1221.000-0.881-0.3960.130-0.077
frequency-0.0020.0900.0790.0800.0360.0180.090-0.8811.0000.234-0.1210.027
qtd_returns-0.0630.3720.2940.3440.242-0.1200.190-0.3960.2341.000-0.0540.210
unique_avg_basket-0.0160.104-0.1810.1470.5160.015-0.6180.130-0.121-0.0541.0000.402
avg_basket-0.1230.5740.1000.7290.383-0.0980.188-0.0770.0270.2100.4021.000

Missing values

2023-06-11T16:37:37.528762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-11T16:37:37.785257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idmonetaryqtd_comprasqtd_itemsqtd_prodsrecencyavg_ticketavg_recency_daysfrequencyqtd_returnsunique_avg_basketavg_basket
112347.04310.0072458182223.68131960.8333330.0191260.014.714286351.142857
212348.01437.2442332277553.23111194.3333330.0140850.05.250000583.000000
512352.01385.747526773617.99662343.3333330.02682063.08.14285775.142857
912356.02487.4331573582242.886724151.5000000.0098680.017.333333524.333333
1112358.0928.06224217154.591765149.0000000.0133330.06.000000121.000000
1212359.06372.58416222485725.69588764.8000000.01454510.053.500000405.500000
1312360.02302.06311561265218.27031774.0000000.0201340.034.666667385.333333
1512362.04737.23102197256318.50480524.3333330.03413017.020.000000219.700000
1612364.01208.104149981714.91481535.0000000.0377360.017.250000374.750000
1912370.03425.69423501665120.636687103.0000000.0129030.035.500000587.500000
customer_idmonetaryqtd_comprasqtd_itemsqtd_prodsrecencyavg_ticketavg_recency_daysfrequencyqtd_returnsunique_avg_basketavg_basket
431718269.0168.60176736624.0857148.0000001.0000006.07.00000076.000000
431818270.0283.152101113825.740909114.0000000.0087343.05.50000050.500000
431918272.03078.5862050166218.54566340.6666670.0244906.016.500000341.666667
432018273.0204.003803268.000000127.5000000.0117190.00.33333326.666667
432118274.0175.92188113015.99272713.0000001.00000088.011.00000088.000000
432218276.0335.861186144323.99000022.0000001.0000002.014.000000186.000000
432318277.0110.3816885813.797500260.0000001.0000001.08.00000068.000000
432718282.0178.05210312714.83750059.5000000.0166675.06.00000051.500000
432818283.02088.9316139575432.77046425.6923080.0477610.016.37500087.187500
432918287.01837.2831586704226.24685779.5000000.0187500.019.666667528.666667